code: def _generate_plot_from_fpr_tpr_lists(fpr, tpr, ax, label=None):
      """
      Draws a plot for every list of values i.e tpr[i] and fpr[i].
      """
      for i in range(len(fpr)):
          fpr_ = fpr[i]
          tpr_ = tpr[i]
          label_ = (
              label[i] if label is not None and len(label) > 0 else None
          )
          _plot_roc(fpr_, tpr_, ax, label=label_)
  
imports: import numpy
  import matplotlib.pyplot
  from sklearn.metrics import roc_curve, auc
  from sklearn.preprocessing import label_binarize
  from ..telemetry import SKLearnEvaluationLogger
  from ..util import is_column_vector, is_row_vector
  from sklearn_evaluation import __version__
  import json
  from pathlib import Path
  from sklearn_evaluation.plot.plot import AbstractPlot, AbstractComposedPlot
  from ploomber_core.exceptions import modify_exceptions
label_desc: Use image node_id 5 for calling a global function with example usage: _generate_plot_from_fpr_tpr_lists(fpr, tpr, ax, label) without return types